In general, the theoretical capacity of a Multiple-Input Multiple-Output (MIMO) communication system with M transmitting antennas and N receiving antennas is greater than the theoretical capacity of a system with a single transmitting/receiving antenna by approximately min(M, N) folds.
However, when multiple antennas are installed in a receiver such as a user terminal, various issues may arise, such as high cost and an inability to secure sufficient distance between the antennas. Generally, there is a limit to the number of receiving antennas that can be installed in the receiver such as the user terminal. However, limiting the number of receiving antennas can limit the theoretical capacity.
Currently, various types of technologies have been proposed to overcome the above problems. A representative technology is a multi-user MIMO system technology. According to the multi-user MIMO system technology, the capacity may be increased as the number of multiple transmitting antennas installed in a base station increases.
In order to increase the capacity as the number of multiple transmitting antennas increases, the base station should be aware of the channel state of user terminals and generate a transmission signal based on the channel state. In order for the base station to sufficiently detect the channel state, complex hardware may be required and wireless resources may need to be consumed. Among the transmission schemes, a zero-forcing beamforming (ZFBF) scheme may achieve high performance with relatively less hardware complexity.
When applying the ZFBF scheme, the user terminal should feed back to the base station channel information that includes channel direction information (CDI) and channel quality information (CQI). For this feedback operation, there are various problems to be solved.
As an example, in a multi-user MIMO system adopting the ZFBF scheme, user terminals may not feed back to the base station the accurate CDI that matches a direction of an actual channel. Generally, each user terminal selects a vector from pre-stored codebook vectors that most matches the direction of the actual channel, and feeds back direction information of the selected vector as the CDI. Therefore, a difference between the direction of the actual channel and the CDI fed back from the user terminal may exist. The difference may be referred to as a quantization error.
As another example, when the user terminals receive data, the base station and the user terminals may not accurately estimate a signal-to-interference and noise ratio (SINR) in each of the user terminals. Since the base station may not accurately estimate the SINR, it may be impossible to set a data transmission rate appropriate for the actual channel.
Accordingly, there is a need for a technology that generates CDI and CQI as close to the accurate values as possible and feed back the generated CDI and the CQI to a base station so that a user terminal may maximize the capacity of a MIMO system.